Rough set-aided keyword reduction for text categorization

Applied Artificial Intelligence

Volumn 15, Issue 9, 2001, Pages 843-873

  Chouchoulas, Alexios ^a   Shen, Qiang ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DATA STRUCTURES; ELECTRONIC MAIL; INFORMATION RETRIEVAL; SYSTEMS ANALYSIS; VECTORS;

AUTOMATED INFORMATION FILTERING; ROUGH SET AIDED KEYWORD REDUCTION; TEXT CATEGORIZATION; TRAINING DATA;

ROUGH SET THEORY;

EID: 0242322799     PISSN: 08839514     EISSN: None     Source Type: Journal    
DOI: 10.1080/088395101753210773     Document Type: Article

References (18)

1. Chouchoulas, A., and Q Shen 1999. A rough set-based approach to text classification. In Proceedings of the Seventh International Workshop on Rough Sets (Lecture Notes in Artificial Intelligence, 1711), 118-127. ISBN 3-540-66645-1.
2. Das-Gupta, P. 1988. Rough sets and information retrieval. In Proceedings of the Eleventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Set Oriented Models, 567-581. Grenoble, France.
3. Hunt, C. 1997. TCP/IP Network Administration, 2nd ed. Sebastopol, CA: O'Reilly and Associates.
4. Jelonek, J , K. Krawiec, and R. Slowinski. 1995. Rough set reduction of attributes and their domains for neural networks Computational Intelligence 11(2).339-347.
5. Kasabov N. K. 1996. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. Cambridge, MA: The MIT Press. ISBN 0-262-11212-4.
6. Keen, D., and A. Rajasekar. 1994. Rough sets and data dependencies. In Proceedings of the Workshop on Incompleteness and Uncertainty in Information Systems (IUIS'93), Workshops in Computing, eds. V. S. Alagar, S. Bergler, and F. Q. Dong, p. 87-101 London: Springer ISBN 3-540-19897-0.
7. Larson, K., and M. Czerwinski 1998 Web page design: Implications of memory, structure and scent for information retrieval. In Proceedings of ACM CHI 1998 Conference on Human Factors in Computing Systems, 25-32, Chicago, IL.
8. Martienne, E., and M Quafafou. 1998. Learning fuzzy relational descriptions using the logical framework and rough set theory. In Proc. of the Seventh IEEE International Conference on Fuzzy Systems. IEEE Press.
9. Mladenić, D., and M. Grobelnik. 1999. Feature selection for unbalanced class distribution and naïve bayes. In Proceedings of the Sixteenth International Conference on Machine Learning, 258-267, Ljubljana, Slovenia.
10. Pawlak, Z. 1982. Rough sets. International Journal of Computer and Information Sciences 11(5):341-356.
11. Pawlak, Z. 1991. Rough Sets: Theoretical Aspects of Reasoning About Data. Dordrecht: Kluwer Academic Publishers.
12. Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
13. Salton, G., and C. Buckley. 1988. Term weighting approaches in automatic text retrieval. Information Processing and Management 24(5):513-523.
14. Shen, Q., and A. Chouchoulas. 1999. Data-driven fuzzy rule induction and its application to systems monitoring. In Proceedings of the Eighth IEEE International Conference on Fuzzy Systems, pages 928-933, Seoul, Korea, August 1999. IEEE Press.
15. Shen, Q., and A. Chouchoulas. 1999. Combining rough sets and data-driven fuzzy learning. Pattern Recognition 32(12):2073-2076.
16. Stefanowski, J., and A. Tsoukiàs. 1999. On the extension of rough sets under incomplete information. In Proceedings of the Seventh International Workshop on Rough Sers (Lecture Notes in Artificial Intelligence, 1711), 73-81. ISBN 3-540-66645-1.
17. van Rijsbergen, C. J. 1979. Information Retrieval. London. Butterworths.
18. Yang, Y., and J. O. Pedersen 1997. A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, 412-420, San Francisco: Morgan Kaufmann Publishers.